Optimal Scheme for Quantum Metrology
- URL: http://arxiv.org/abs/2111.12279v1
- Date: Wed, 24 Nov 2021 05:37:26 GMT
- Title: Optimal Scheme for Quantum Metrology
- Authors: Jing Liu, Mao Zhang, Hongzhen Chen, Lingna Wang, Haidong Yuan
- Abstract summary: The state preparation, parametrization, and measurement steps are reviewed.
It is hoped this provides a useful reference for the researchers in quantum metrology and related fields.
- Score: 4.682128305250867
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Quantum metrology can achieve far better precision than classical metrology,
and is one of the most important applications of quantum technologies in the
real world. To attain the highest precision promised by quantum metrology, all
steps of the schemes need to be optimized, which include the state preparation,
parametrization, and measurement. Here the recent progresses on the
optimization of these steps, which are essential for the identification and
achievement of the ultimate precision limit in quantum metrology, are reviewed.
It is hoped this provides a useful reference for the researchers in quantum
metrology and related fields.
Related papers
- Quantum metrology with a continuous-variable system [0.0]
We discuss precision limits and optimal strategies in quantum metrology and sensing with a single mode of quantum continuous variables.
We summarize some of the main experimental achievements and present emerging platforms for continuous-variable sensing.
arXiv Detail & Related papers (2024-11-06T18:57:07Z) - Fast Quantum Process Tomography via Riemannian Gradient Descent [3.1406146587437904]
Constrained optimization plays a crucial role in the fields of quantum physics and quantum information science.
One specific issue is that of quantum process tomography, in which the goal is to retrieve the underlying quantum process based on a given set of measurement data.
arXiv Detail & Related papers (2024-04-29T16:28:14Z) - Finding the optimal probe state for multiparameter quantum metrology
using conic programming [61.98670278625053]
We present a conic programming framework that allows us to determine the optimal probe state for the corresponding precision bounds.
We also apply our theory to analyze the canonical field sensing problem using entangled quantum probe states.
arXiv Detail & Related papers (2024-01-11T12:47:29Z) - Assessing requirements to scale to practical quantum advantage [56.22441723982983]
We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications.
We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage.
A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
arXiv Detail & Related papers (2022-11-14T18:50:27Z) - Tight Cram\'{e}r-Rao type bounds for multiparameter quantum metrology
through conic programming [61.98670278625053]
It is paramount to have practical measurement strategies that can estimate incompatible parameters with best precisions possible.
Here, we give a concrete way to find uncorrelated measurement strategies with optimal precisions.
We show numerically that there is a strict gap between the previous efficiently computable bounds and the ultimate precision bound.
arXiv Detail & Related papers (2022-09-12T13:06:48Z) - Approaching optimal entangling collective measurements on quantum
computing platforms [0.3665899982351484]
We show theoretically optimal single- and two-copy collective measurements for simultaneously estimating two non-commuting qubit rotations.
This allows us to implement quantum-enhanced sensing, for which the metrological gain persists for high levels of decoherence.
arXiv Detail & Related papers (2022-05-30T18:07:27Z) - An Introduction to Quantum Machine Learning for Engineers [36.18344598412261]
Quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers.
This book provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra.
arXiv Detail & Related papers (2022-05-11T12:10:52Z) - Quantum Information Techniques for Quantum Metrology [0.0]
Main goal of quantum metrology is to estimate unknown parameters as accurately as possible.
By using quantum resources as probes, it is possible to attain a measurement precision that would be otherwise impossible using the best classical strategies.
This thesis explores how quantum metrology can be enhanced with other quantum techniques when appropriate.
arXiv Detail & Related papers (2022-01-05T10:19:25Z) - Quantum metrology for non-Markovian processes [3.5509551353363644]
In this Letter, we establish a general framework of non-Markovian quantum metrology.
For any parametrized non-Markovian process on a finite-dimensional system, we derive a formula for the maximal amount of quantum Fisher information.
We design an algorithm that evaluates this quantum Fisher information via semidefinite programming.
arXiv Detail & Related papers (2021-03-03T19:00:06Z) - Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations.
We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
arXiv Detail & Related papers (2020-12-08T01:10:30Z) - Fast and robust quantum state tomography from few basis measurements [65.36803384844723]
We present an online tomography algorithm designed to optimize all the aforementioned resources at the cost of a worse dependence on accuracy.
The protocol is the first to give provably optimal performance in terms of rank and dimension for state copies, measurement settings and memory.
Further improvements are possible by executing the algorithm on a quantum computer, giving a quantum speedup for quantum state tomography.
arXiv Detail & Related papers (2020-09-17T11:28:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.